Medical device manufacturing as an industry segment is fast evolving towards Industry 4.0. It also remain probably one of the most regulated industries all over the world and individualization of products is happening faster here than in other industries.
In the medical device industry products are being increasingly customized, either due to demand or due to market specific regulations. Increasingly production is moving towards a lot size of one. In such an environment, manufacturers have a lot to lose if a product has to be scrapped, recalled, replaced or a claim is to be paid under warranty.
The requirements of modern medical devices have moved on from mere quality control to a quality lifecycle management of sorts. The monitoring of quality of a product begins as the parts used to manufacture it are being assembled and its performance is monitored through its lifecycle even months and years after it is deployed in the field.
To make product quality predictive in nature the process IT, specifically the MES, needs to be configured and deployed in such a way that it deploys advanced analytics on data collected and predicts issues which are far from happening.
First things first, the availability of data plays a key role in allowing predictive management to kick-in, thereby application like the MES should be allowed access to data upstream, generated at the customer’s end. This is possible through free flow of data from products deployed in the field through smart sensors and IIoT and application interfaces. Coupled with field data and the data being collected for every single event and transaction on the shop-floor, the MES applies most modern analytical tools to unearth patterns which lead to frequent faults and performs risk assessment to predict risk, before that risk can become a reality.
Imagine that the MES deployed in a prosthetic manufacturing unit is allowed access to all previous quality claims, rejections and warranty claims data. If the MES is configured for I 4.0, it would be able to apply predictive statistical models collating data present in claims history and current activities on the shop-floor and risk related analytical insights. Once any sort of trend or patter highlighting future risk or current corrective action is established, the MES should alert process owners to take action requisite to solve the future error. Once the action is executed, a follow-up report ensures that a particular issue or a cluster of probable issues have been countered.
If the above explained system prevails, our prosthetic manufacturer can clearly and distinctly reduce scarp, defects, recalls and claims.
What is critical for manufacturers to realize when it comes to predictive product quality management is that is goes above and beyond quality control and CAPA management. The life cycle of quality is now beyond the shop-floor and flows throughout the value chain. MES applications should be able to use the big data thrown towards it and allow changes to be made in a predictive manner, not just in the shop-floor of the main manufacturer, but the information should disseminate and elicit action even in secondary and tertiary levels of the supply chain.
Medical device manufacturing being highly regulated industry also benefits a great deal in compliance with an I 4.0-ready MES, as quality is not just made predictive, it is also recorded and reported in requisite compliance formats, allowing both process improvement and compliance to achieved continually.
Industry leaders have already deployed I 4.0 and are experiencing the benefits from predictive product quality management, mainly reduced downtime, reduced recalls, reduced scrap, reduced claims, reduced effort in compliance, better supply chain performance, higher customer satisfaction and overall reduction is cost of quality.
In the medical device industry products are being increasingly customized, either due to demand or due to market specific regulations. Increasingly production is moving towards a lot size of one. In such an environment, manufacturers have a lot to lose if a product has to be scrapped, recalled, replaced or a claim is to be paid under warranty.
The requirements of modern medical devices have moved on from mere quality control to a quality lifecycle management of sorts. The monitoring of quality of a product begins as the parts used to manufacture it are being assembled and its performance is monitored through its lifecycle even months and years after it is deployed in the field.
To make product quality predictive in nature the process IT, specifically the MES, needs to be configured and deployed in such a way that it deploys advanced analytics on data collected and predicts issues which are far from happening.
First things first, the availability of data plays a key role in allowing predictive management to kick-in, thereby application like the MES should be allowed access to data upstream, generated at the customer’s end. This is possible through free flow of data from products deployed in the field through smart sensors and IIoT and application interfaces. Coupled with field data and the data being collected for every single event and transaction on the shop-floor, the MES applies most modern analytical tools to unearth patterns which lead to frequent faults and performs risk assessment to predict risk, before that risk can become a reality.
Imagine that the MES deployed in a prosthetic manufacturing unit is allowed access to all previous quality claims, rejections and warranty claims data. If the MES is configured for I 4.0, it would be able to apply predictive statistical models collating data present in claims history and current activities on the shop-floor and risk related analytical insights. Once any sort of trend or patter highlighting future risk or current corrective action is established, the MES should alert process owners to take action requisite to solve the future error. Once the action is executed, a follow-up report ensures that a particular issue or a cluster of probable issues have been countered.
If the above explained system prevails, our prosthetic manufacturer can clearly and distinctly reduce scarp, defects, recalls and claims.
What is critical for manufacturers to realize when it comes to predictive product quality management is that is goes above and beyond quality control and CAPA management. The life cycle of quality is now beyond the shop-floor and flows throughout the value chain. MES applications should be able to use the big data thrown towards it and allow changes to be made in a predictive manner, not just in the shop-floor of the main manufacturer, but the information should disseminate and elicit action even in secondary and tertiary levels of the supply chain.
Medical device manufacturing being highly regulated industry also benefits a great deal in compliance with an I 4.0-ready MES, as quality is not just made predictive, it is also recorded and reported in requisite compliance formats, allowing both process improvement and compliance to achieved continually.
Industry leaders have already deployed I 4.0 and are experiencing the benefits from predictive product quality management, mainly reduced downtime, reduced recalls, reduced scrap, reduced claims, reduced effort in compliance, better supply chain performance, higher customer satisfaction and overall reduction is cost of quality.
